How Startkeel calculates — and how we know it's right

The engine

There's no secret in the math. Startkeel uses the standard SaaS formulas — the same ones every serious operator uses — applied correctly, kept consistent across every view (calculator, score, full model), and checked against real companies. A monthly model projects revenue, costs, P&L and cash for up to 3 years from your inputs. The formulas doing the work:

• Runway = cash ÷ current net burn (burn measured over the first months from today, not averaged across an already-profitable horizon).
• Default alive = you reach break-even before cash hits zero (the metric that actually decides whether you survive).
• LTV = ARPA × gross margin ÷ monthly churn (lifetime capped at 60 months).
• CAC = (acquisition spend + sales salaries, if sales-led) ÷ new customers, over the whole period — not just month one.
• CAC payback = CAC ÷ (ARPA × gross margin).
• NRR (annual) = (1 + expansion − churn)¹², so it's on the same axis as the annual benchmark.
• Burn multiple = net burn ÷ net new ARR (Sacks).

Benchmarks, with sources

Every metric is judged against reference ranges for early-stage SaaS — read by stage, not as absolute rules. The ranges are built from public SaaS metrics literature:

• · David Skok (forEntrepreneurs) — unit economics, LTV:CAC, payback.
• · Christoph Janz (Point Nine) — the SaaS Funding Napkin, ARPA segmentation.
• · Dave Kellogg — NRR / net revenue retention.
• · David Sacks — the burn multiple framework.
• · MicroConf — State of Independent SaaS (bootstrapped data).

Our growth check (the part templates miss)

Most tools flag “high growth” with a flat percentage. That's wrong: 37% month-over-month from $1K MRR is a real, healthy ramp; the same 37% sustained from $100K MRR is a hockey-stick fantasy. Startkeel scales the growth tolerance to your revenue base, so it tells the two apart — something a spreadsheet doesn't do and a generic LLM won't catch.

Validated against real startups

This is where the trust comes from — not the formulas, but whether the output matches reality. We run real, public startups through the engine and check the numbers hold up. A growing set we use as regression tests:

Every case we validate gets added as a regression test, so the criteria only get sharper over time. Beyond named cases, we also check the engine's outputs land within published industry ranges — a sales-led SMB's CAC payback, for instance, comes out in the 8–24 month band that benchmark studies report (and only stays realistic because we count sales salaries in CAC, not just ad spend).

Based on publicly reported figures; assumptions are labeled. We're not affiliated with or endorsed by these companies — this is our own independent analysis.

Assumptions & simplifications

A model is only as honest as its assumptions. Startkeel is built for early-stage SaaS viability, so it deliberately keeps things simple where full FP&A would add noise, not value. What we simplify — on purpose:

• · Cash ≈ recognized MRR (monthly). We model revenue as it's earned each month. If you bill annually upfront, your real cash arrives in lumps — the model spreads it, so it's conservative on early cash. Deferred revenue and working capital aren't modeled.
• · LTV uses logo churn, capped at 60 months. Customer lifetime is 1 ÷ monthly churn, capped at 5 years — because no company realistically captures a decade-plus relationship (a number with no story). It ignores expansion, so it's a conservative LTV.
• · Flat ARPA, no cohorts. We assume a constant ARPA rather than modeling per-cohort decay/expansion — fine for a viability read, less precise than a full cohort model.
• · No taxes, no balance sheet. Pre-seed is usually loss-making (no profit tax) and asset-light (no inventory/working-capital schedule). We skip both on purpose.

These are simplifications, not errors — declared so you know exactly what the number does and doesn't include. A real CFO would note them; for an early-stage viability call, adding them would be noise.

What it is — and isn't

Startkeel gives you estimates to make better decisions — not certainty, and not financial or investment advice. Early-stage numbers are noisy; the value is correctness plus honest criteria, not false precision. When a figure is an assumption (e.g. CAC without your real acquisition spend), we say so rather than quietly inflate it.

FAQ